"""simple docstring"""






from dataclasses import dataclass
from typing import Optional

import numpy as np
import torch
import torch.nn as nn

from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class 	UpperCAmelCase__					(     __A      ):


	"""simple docstring"""
	lowerCAmelCase__     :       torch.FloatTensor




class 	UpperCAmelCase__					(     nn.Module      ):


	"""simple docstring"""







	def __init__(      self							,  _SCREAMING_SNAKE_CASE=3							,  _SCREAMING_SNAKE_CASE=3							,  _SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",)							,  _SCREAMING_SNAKE_CASE=(6_4,)							,  _SCREAMING_SNAKE_CASE=2							,  _SCREAMING_SNAKE_CASE=3_2							,  _SCREAMING_SNAKE_CASE="silu"							,  _SCREAMING_SNAKE_CASE=True							,  )     ->						Optional[Any]:
							super().__init__()
							a_				:      Optional[int]  		=       layers_per_block

							a_				:      int  		=       torch.nn.Convad(
							    UpperCamelCase__							,  block_out_channels[0]							,  kernel_size=3							,  stride=1							,  padding=1							,  )

							a_				:      Union[str, Any]  		=       None
							a_				:      Optional[int]  		=       nn.ModuleList([]			)

							# down
							a_				:      Union[str, Any]  		=       block_out_channels[0]
							for i, down_block_type in enumerate(UpperCamelCase__			):
													a_				:      Any  		=       output_channel
													a_				:      Optional[Any]  		=       block_out_channels[i]
													a_				:      Union[str, Any]  		=       i == len(UpperCamelCase__			) - 1

													a_				:      Any  		=       get_down_block(
													    UpperCamelCase__							,  num_layers=self.layers_per_block							,  in_channels=UpperCamelCase__							,  out_channels=UpperCamelCase__							,  add_downsample=not is_final_block							,  resnet_eps=1E-6							,  downsample_padding=0							,  resnet_act_fn=UpperCamelCase__							,  resnet_groups=UpperCamelCase__							,  attention_head_dim=UpperCamelCase__							,  temb_channels=UpperCamelCase__							,  )
													self.down_blocks.append(UpperCamelCase__			)

							# mid
							a_				:      Dict  		=       UNetMidBlockaD(
							    in_channels=block_out_channels[-1]							,  resnet_eps=1E-6							,  resnet_act_fn=UpperCamelCase__							,  output_scale_factor=1							,  resnet_time_scale_shift="default"							,  attention_head_dim=block_out_channels[-1]							,  resnet_groups=UpperCamelCase__							,  temb_channels=UpperCamelCase__							,  )

							# out
							a_				:      List[Any]  		=       nn.GroupNorm(num_channels=block_out_channels[-1]							,  num_groups=UpperCamelCase__							,  eps=1E-6			)
							a_				:      Tuple  		=       nn.SiLU()

							a_				:      List[Any]  		=       2 * out_channels if double_z else out_channels
							a_				:      Optional[int]  		=       nn.Convad(block_out_channels[-1]							,  UpperCamelCase__							,  3							,  padding=1			)

							a_				:      Union[str, Any]  		=       False







	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						Optional[int]:
							a_				:      str  		=       x
							a_				:      Dict  		=       self.conv_in(UpperCamelCase__			)

							if self.training and self.gradient_checkpointing:

													def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																			def custom_forward(*_SCREAMING_SNAKE_CASE			):
																									return module(*UpperCamelCase__			)

																			return custom_forward

													# down
													if is_torch_version(">="							,  "1.11.0"			):
																			for down_block in self.down_blocks:
																									a_				:      Optional[int]  		=       torch.utils.checkpoint.checkpoint(
																									    create_custom_forward(UpperCamelCase__			)							,  UpperCamelCase__							,  use_reentrant=UpperCamelCase__			)
																			# middle
																			a_				:      Tuple  		=       torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block			)							,  UpperCamelCase__							,  use_reentrant=UpperCamelCase__			)
													else:
																			for down_block in self.down_blocks:
																									a_				:      List[Any]  		=       torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__			)							,  UpperCamelCase__			)
																			# middle
																			a_				:      Optional[Any]  		=       torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block			)							,  UpperCamelCase__			)

							else:
													# down
													for down_block in self.down_blocks:
																			a_				:      Tuple  		=       down_block(UpperCamelCase__			)

													# middle
													a_				:      Union[str, Any]  		=       self.mid_block(UpperCamelCase__			)

							# post-process
							a_				:      str  		=       self.conv_norm_out(UpperCamelCase__			)
							a_				:      str  		=       self.conv_act(UpperCamelCase__			)
							a_				:      List[Any]  		=       self.conv_out(UpperCamelCase__			)

							return sample




class 	UpperCAmelCase__					(     nn.Module      ):


	"""simple docstring"""







	def __init__(      self							,  _SCREAMING_SNAKE_CASE=3							,  _SCREAMING_SNAKE_CASE=3							,  _SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",)							,  _SCREAMING_SNAKE_CASE=(6_4,)							,  _SCREAMING_SNAKE_CASE=2							,  _SCREAMING_SNAKE_CASE=3_2							,  _SCREAMING_SNAKE_CASE="silu"							,  _SCREAMING_SNAKE_CASE="group"							,  )     ->						Optional[Any]:
							super().__init__()
							a_				:      Dict  		=       layers_per_block

							a_				:      Dict  		=       nn.Convad(
							    UpperCamelCase__							,  block_out_channels[-1]							,  kernel_size=3							,  stride=1							,  padding=1							,  )

							a_				:      Tuple  		=       None
							a_				:      List[str]  		=       nn.ModuleList([]			)

							a_				:      Optional[Any]  		=       in_channels if norm_type == "spatial" else None

							# mid
							a_				:      str  		=       UNetMidBlockaD(
							    in_channels=block_out_channels[-1]							,  resnet_eps=1E-6							,  resnet_act_fn=UpperCamelCase__							,  output_scale_factor=1							,  resnet_time_scale_shift="default" if norm_type == "group" else norm_type							,  attention_head_dim=block_out_channels[-1]							,  resnet_groups=UpperCamelCase__							,  temb_channels=UpperCamelCase__							,  )

							# up
							a_				:      Optional[int]  		=       list(reversed(UpperCamelCase__			)			)
							a_				:      Optional[int]  		=       reversed_block_out_channels[0]
							for i, up_block_type in enumerate(UpperCamelCase__			):
													a_				:      int  		=       output_channel
													a_				:      Optional[int]  		=       reversed_block_out_channels[i]

													a_				:      int  		=       i == len(UpperCamelCase__			) - 1

													a_				:      Any  		=       get_up_block(
													    UpperCamelCase__							,  num_layers=self.layers_per_block + 1							,  in_channels=UpperCamelCase__							,  out_channels=UpperCamelCase__							,  prev_output_channel=UpperCamelCase__							,  add_upsample=not is_final_block							,  resnet_eps=1E-6							,  resnet_act_fn=UpperCamelCase__							,  resnet_groups=UpperCamelCase__							,  attention_head_dim=UpperCamelCase__							,  temb_channels=UpperCamelCase__							,  resnet_time_scale_shift=UpperCamelCase__							,  )
													self.up_blocks.append(UpperCamelCase__			)
													a_				:      List[Any]  		=       output_channel

							# out
							if norm_type == "spatial":
													a_				:      List[Any]  		=       SpatialNorm(block_out_channels[0]							,  UpperCamelCase__			)
							else:
													a_				:      List[str]  		=       nn.GroupNorm(num_channels=block_out_channels[0]							,  num_groups=UpperCamelCase__							,  eps=1E-6			)
							a_				:      Union[str, Any]  		=       nn.SiLU()
							a_				:      int  		=       nn.Convad(block_out_channels[0]							,  UpperCamelCase__							,  3							,  padding=1			)

							a_				:      Optional[Any]  		=       False







	def    A     (      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE=None			)     ->						Optional[Any]:
							a_				:      Any  		=       z
							a_				:      Tuple  		=       self.conv_in(UpperCamelCase__			)

							a_				:      List[Any]  		=       next(iter(self.up_blocks.parameters()			)			).dtype
							if self.training and self.gradient_checkpointing:

													def create_custom_forward(_SCREAMING_SNAKE_CASE			):
																			def custom_forward(*_SCREAMING_SNAKE_CASE			):
																									return module(*UpperCamelCase__			)

																			return custom_forward

													if is_torch_version(">="							,  "1.11.0"			):
																			# middle
																			a_				:      Union[str, Any]  		=       torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block			)							,  UpperCamelCase__							,  UpperCamelCase__							,  use_reentrant=UpperCamelCase__			)
																			a_				:      Tuple  		=       sample.to(UpperCamelCase__			)

																			# up
																			for up_block in self.up_blocks:
																									a_				:      str  		=       torch.utils.checkpoint.checkpoint(
																									    create_custom_forward(UpperCamelCase__			)							,  UpperCamelCase__							,  UpperCamelCase__							,  use_reentrant=UpperCamelCase__			)
													else:
																			# middle
																			a_				:      Tuple  		=       torch.utils.checkpoint.checkpoint(
																			    create_custom_forward(self.mid_block			)							,  UpperCamelCase__							,  UpperCamelCase__			)
																			a_				:      int  		=       sample.to(UpperCamelCase__			)

																			# up
																			for up_block in self.up_blocks:
																									a_				:      Optional[Any]  		=       torch.utils.checkpoint.checkpoint(create_custom_forward(UpperCamelCase__			)							,  UpperCamelCase__							,  UpperCamelCase__			)
							else:
													# middle
													a_				:      Union[str, Any]  		=       self.mid_block(UpperCamelCase__							,  UpperCamelCase__			)
													a_				:      List[Any]  		=       sample.to(UpperCamelCase__			)

													# up
													for up_block in self.up_blocks:
																			a_				:      Optional[int]  		=       up_block(UpperCamelCase__							,  UpperCamelCase__			)

        # post-process
							if latent_embeds is None:
													a_				:      List[str]  		=       self.conv_norm_out(UpperCamelCase__			)
							else:
													a_				:      List[Any]  		=       self.conv_norm_out(UpperCamelCase__							,  UpperCamelCase__			)
							a_				:      Optional[Any]  		=       self.conv_act(UpperCamelCase__			)
							a_				:      Dict  		=       self.conv_out(UpperCamelCase__			)

							return sample




class 	UpperCAmelCase__					(     nn.Module      ):


	"""simple docstring"""







	def __init__(      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE=None							,  _SCREAMING_SNAKE_CASE="random"							,  _SCREAMING_SNAKE_CASE=False							,  _SCREAMING_SNAKE_CASE=True			)     ->						Optional[int]:
							super().__init__()
							a_				:      Union[str, Any]  		=       n_e
							a_				:      List[str]  		=       vq_embed_dim
							a_				:      Any  		=       beta
							a_				:      Optional[int]  		=       legacy

							a_				:      Optional[int]  		=       nn.Embedding(self.n_e							,  self.vq_embed_dim			)
							self.embedding.weight.data.uniform_(-1.0 / self.n_e							,  1.0 / self.n_e			)

							a_				:      Tuple  		=       remap
							if self.remap is not None:
													self.register_buffer("used"							,  torch.tensor(np.load(self.remap			)			)			)
													a_				:      List[str]  		=       self.used.shape[0]
													a_				:      Optional[Any]  		=       unknown_index  # "random" or "extra" or integer
													if self.unknown_index == "extra":
																			a_				:      List[Any]  		=       self.re_embed
																			a_				:      Tuple  		=       self.re_embed + 1
													print(
													    f'''Remapping {self.n_e} indices to {self.re_embed} indices. '''
													    f'''Using {self.unknown_index} for unknown indices.'''			)
							else:
													a_				:      Dict  		=       n_e

							a_				:      List[str]  		=       sane_index_shape







	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						Union[str, Any]:
							a_				:      List[Any]  		=       inds.shape
							assert len(UpperCamelCase__			) > 1
							a_				:      Optional[int]  		=       inds.reshape(ishape[0]							,  -1			)
							a_				:      List[str]  		=       self.used.to(UpperCamelCase__			)
							a_				:      Optional[Any]  		=       (inds[:, :, None] == used[None, None, ...]).long()
							a_				:      Dict  		=       match.argmax(-1			)
							a_				:      int  		=       match.sum(2			) < 1
							if self.unknown_index == "random":
													a_				:      str  		=       torch.randint(0							,  self.re_embed							,  size=new[unknown].shape			).to(device=new.device			)
							else:
													a_				:      Optional[Any]  		=       self.unknown_index
							return new.reshape(UpperCamelCase__			)







	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						Any:
							a_				:      Dict  		=       inds.shape
							assert len(UpperCamelCase__			) > 1
							a_				:      Union[str, Any]  		=       inds.reshape(ishape[0]							,  -1			)
							a_				:      Optional[int]  		=       self.used.to(UpperCamelCase__			)
							if self.re_embed > self.used.shape[0]:  # extra token
													a_				:      List[str]  		=       0  # simply set to zero
							a_				:      str  		=       torch.gather(used[None, :][inds.shape[0] * [0], :]							,  1							,  UpperCamelCase__			)
							return back.reshape(UpperCamelCase__			)







	def    A     (      self							,  _SCREAMING_SNAKE_CASE			)     ->						List[Any]:
							a_				:      Optional[int]  		=       z.permute(0							,  2							,  3							,  1			).contiguous()
							a_				:      str  		=       z.view(-1							,  self.vq_embed_dim			)

							# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
							a_				:      List[Any]  		=       torch.argmin(torch.cdist(UpperCamelCase__							,  self.embedding.weight			)							,  dim=1			)

							a_				:      Tuple  		=       self.embedding(UpperCamelCase__			).view(z.shape			)
							a_				:      Union[str, Any]  		=       None
							a_				:      int  		=       None

							# compute loss for embedding
							if not self.legacy:
													a_				:      str  		=       self.beta * torch.mean((z_q.detach() - z) ** 2			) + torch.mean((z_q - z.detach()) ** 2			)
							else:
													a_				:      Union[str, Any]  		=       torch.mean((z_q.detach() - z) ** 2			) + self.beta * torch.mean((z_q - z.detach()) ** 2			)

							# preserve gradients
							a_				:      List[str]  		=       z + (z_q - z).detach()

							# reshape back to match original input shape
							a_				:      int  		=       z_q.permute(0							,  3							,  1							,  2			).contiguous()

							if self.remap is not None:
													a_				:      Optional[int]  		=       min_encoding_indices.reshape(z.shape[0]							,  -1			)  # add batch axis
													a_				:      List[Any]  		=       self.remap_to_used(UpperCamelCase__			)
													a_				:      int  		=       min_encoding_indices.reshape(-1							,  1			)  # flatten

							if self.sane_index_shape:
													a_				:      Union[str, Any]  		=       min_encoding_indices.reshape(z_q.shape[0]							,  z_q.shape[2]							,  z_q.shape[3]			)

							return z_q, loss, (perplexity, min_encodings, min_encoding_indices)







	def    A     (      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE			)     ->						Any:


							if self.remap is not None:
													a_				:      int  		=       indices.reshape(shape[0]							,  -1			)  # add batch axis
													a_				:      int  		=       self.unmap_to_all(UpperCamelCase__			)
													a_				:      Dict  		=       indices.reshape(-1			)  # flatten again

							# get quantized latent vectors
							a_				:      Any  		=       self.embedding(UpperCamelCase__			)

							if shape is not None:
													a_				:      int  		=       z_q.view(UpperCamelCase__			)
													# reshape back to match original input shape
													a_				:      Dict  		=       z_q.permute(0							,  3							,  1							,  2			).contiguous()

							return z_q




class 	UpperCAmelCase__					(     __A      ):


	"""simple docstring"""







	def __init__(      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE=False			)     ->						Any:
							a_				:      Dict  		=       parameters
							a_		,       a_				:      Dict  		=       torch.chunk(UpperCamelCase__							,  2							,  dim=1			)
							a_				:      Dict  		=       torch.clamp(self.logvar							,  -3_0.0							,  2_0.0			)
							a_				:      Tuple  		=       deterministic
							a_				:      Any  		=       torch.exp(0.5 * self.logvar			)
							a_				:      int  		=       torch.exp(self.logvar			)
							if self.deterministic:
													a_				:      Optional[int]  		=       torch.zeros_like(
													    self.mean							,  device=self.parameters.device							,  dtype=self.parameters.dtype			)







	def    A     (      self							,  _SCREAMING_SNAKE_CASE = None			)     ->						Optional[Any]:
							a_				:      Union[str, Any]  		=       randn_tensor(
							    self.mean.shape							,  generator=UpperCamelCase__							,  device=self.parameters.device							,  dtype=self.parameters.dtype			)
							a_				:      Dict  		=       self.mean + self.std * sample
							return x







	def    A     (      self							,  _SCREAMING_SNAKE_CASE=None			)     ->						Any:


							if self.deterministic:
													return torch.Tensor([0.0]			)
							else:
													if other is None:
																			return 0.5 * torch.sum(torch.pow(self.mean							,  2			) + self.var - 1.0 - self.logvar							,  dim=[1, 2, 3]			)
													else:
																			return 0.5 * torch.sum(
																			    torch.pow(self.mean - other.mean							,  2			) / other.var
																			    + self.var / other.var
																			    - 1.0
																			    - self.logvar
																			    + other.logvar							,  dim=[1, 2, 3]							,  )







	def    A     (      self							,  _SCREAMING_SNAKE_CASE							,  _SCREAMING_SNAKE_CASE=[1, 2, 3]			)     ->						int:


							if self.deterministic:
													return torch.Tensor([0.0]			)
							a_				:      Dict  		=       np.log(2.0 * np.pi			)
							return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean							,  2			) / self.var							,  dim=UpperCamelCase__			)







	def    A     (      self			)     ->						List[str]:
							return self.mean












'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import (
    IMAGE_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()




class   __SCREAMING_SNAKE_CASE       (__A							,       __A							,       unittest.TestCase       ):


  """simple docstring"""


  _a  : str      			=     CycleDiffusionPipeline
  _a  : int      			=     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {
      '''negative_prompt''',
      '''height''',
      '''width''',
      '''negative_prompt_embeds''',
  }
  _a  : str      			=     PipelineTesterMixin.required_optional_params - {'''latents'''}
  _a  : str      			=     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({'''source_prompt'''}       )
  _a  : Optional[Any]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS
  _a  : Optional[int]      			=     IMAGE_TO_IMAGE_IMAGE_PARAMS







  def    _a       (			self	):






         """simple docstring"""


         torch.manual_seed(0	)
         a_						     =			UNetaDConditionModel(
             block_out_channels=(32, 64)   ,	layers_per_block=2   ,	sample_size=32   ,	in_channels=4   ,	out_channels=4   ,	down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')   ,	up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')   ,	cross_attention_dim=32   ,	)
         a_						     =			DDIMScheduler(
             beta_start=0.00_085   ,	beta_end=0.012   ,	beta_schedule='scaled_linear'   ,	num_train_timesteps=1_000   ,	clip_sample=UpperCamelCase__   ,	set_alpha_to_one=UpperCamelCase__   ,	)
         torch.manual_seed(0	)
         a_						     =			AutoencoderKL(
             block_out_channels=[32, 64]   ,	in_channels=3   ,	out_channels=3   ,	down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']   ,	up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']   ,	latent_channels=4   ,	)
         torch.manual_seed(0	)
         a_						     =			CLIPTextConfig(
             bos_token_id=0   ,	eos_token_id=2   ,	hidden_size=32   ,	intermediate_size=37   ,	layer_norm_eps=1e-05   ,	num_attention_heads=4   ,	num_hidden_layers=5   ,	pad_token_id=1   ,	vocab_size=1_000   ,	)
         a_						     =			CLIPTextModel(UpperCamelCase__	)
         a_						     =			CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'	)

         a_						     =			{
             'unet': unet,
             'scheduler': scheduler,
             'vae': vae,
             'text_encoder': text_encoder,
             'tokenizer': tokenizer,
             'safety_checker': None,
             'feature_extractor': None,
         }
         return components







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=0	):






         """simple docstring"""


         a_						     =			floats_tensor((1, 3, 32, 32)   ,	rng=random.Random(UpperCamelCase__	)	).to(UpperCamelCase__	)
         a_						     =			image / 2 + 0.5
         if str(UpperCamelCase__	).startswith('mps'	):
                a_						     =			torch.manual_seed(UpperCamelCase__	)
         else:
                a_						     =			torch.Generator(device=UpperCamelCase__	).manual_seed(UpperCamelCase__	)
         a_						     =			{
             'prompt': 'An astronaut riding an elephant',
             'source_prompt': 'An astronaut riding a horse',
             'image': image,
             'generator': generator,
             'num_inference_steps': 2,
             'eta': 0.1,
             'strength': 0.8,
             'guidance_scale': 3,
             'source_guidance_scale': 1,
             'output_type': 'numpy',
         }
         return inputs







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			'cpu'  # ensure determinism for the device-dependent torch.Generator

         a_						     =			self.get_dummy_components()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.4_459, 0.4_943, 0.4_544, 0.6_643, 0.5_474, 0.4_327, 0.5_701, 0.5_959, 0.5_179]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @unittest.skipIf(torch_device != 'cuda'   ,	'This test requires a GPU'	)
  def    _a       (			self	):






         """simple docstring"""


         a_						     =			self.get_dummy_components()
         for name, module in components.items():
                if hasattr(UpperCamelCase__   ,	'half'	):
                       a_						     =			module.half()
         a_						     =			CycleDiffusionPipeline(**UpperCamelCase__	)
         a_						     =			pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)

         a_						     =			self.get_dummy_inputs(UpperCamelCase__	)
         a_						     =			pipe(**UpperCamelCase__	)
         a_						     =			output.images

         a_						     =			images[0, -3:, -3:, -1]

         assert images.shape == (1, 32, 32, 3)
         a_						     =			np.array([0.3_506, 0.4_543, 0.446, 0.4_575, 0.5_195, 0.4_155, 0.5_273, 0.518, 0.4_116]	)

         assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-2







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_local()







  @unittest.skip('non-deterministic pipeline'	)
  def    _a       (			self	):






         """simple docstring"""


         return super().test_inference_batch_single_identical()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_dict_tuple_outputs_equivalent()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_save_load_optional_components()







  @skip_mps
  def    _a       (			self	):






         """simple docstring"""


         return super().test_attention_slicing_forward_pass()





@slow
@require_torch_gpu
class   __SCREAMING_SNAKE_CASE       (unittest.TestCase       ):


  """simple docstring"""


  def    _a       (			self	):






         """simple docstring"""


         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car_fp16.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(
             UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__   ,	torch_dtype=torch.floataa   ,	revision='fp16'	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         # the values aren't exactly equal, but the images look the same visually
         assert np.abs(image - expected_image	).max() < 5e-1







  def    _a       (			self	):






         """simple docstring"""


         a_						     =			load_image(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
             '/cycle-diffusion/black_colored_car.png'	)
         a_						     =			load_numpy(
             'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/cycle-diffusion/blue_colored_car.npy'	)
         a_						     =			init_image.resize((512, 512)	)

         a_						     =			'CompVis/stable-diffusion-v1-4'
         a_						     =			DDIMScheduler.from_pretrained(UpperCamelCase__   ,	subfolder='scheduler'	)
         a_						     =			CycleDiffusionPipeline.from_pretrained(UpperCamelCase__   ,	scheduler=UpperCamelCase__   ,	safety_checker=UpperCamelCase__	)

         pipe.to(UpperCamelCase__	)
         pipe.set_progress_bar_config(disable=UpperCamelCase__	)
         pipe.enable_attention_slicing()

         a_						     =			'A black colored car'
         a_						     =			'A blue colored car'

         a_						     =			torch.manual_seed(0	)
         a_						     =			pipe(
             prompt=UpperCamelCase__   ,	source_prompt=UpperCamelCase__   ,	image=UpperCamelCase__   ,	num_inference_steps=100   ,	eta=0.1   ,	strength=0.85   ,	guidance_scale=3   ,	source_guidance_scale=1   ,	generator=UpperCamelCase__   ,	output_type='np'   ,	)
         a_						     =			output.images

         assert np.abs(image - expected_image	).max() < 2e-2




